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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Zenghui Zhang Kaile Zhou Shanlin Yang Key Laboratory of Process Optimization and Intelligent Decision-Making School of Management Hefei University of Technology Hefei China

An important figure-of-merit for battery energy storage systems (BESSs) is their battery life, which is measured by the state of health (SOH). In this study, we propose a two-stage model to optimize the charging and discharging process of BESS in an industrial park microgrid (IPM). The first stage is used to optimize the charging and discharging time and the corresponding amount of the charging and discharging energy from the BESS, in which the SOH is considered. Subsequently, all battery packs need to be coordinated to ensure a reasonable BESS operation. In order to prolong the service life of the BESS, the second stage of the model allocates the charging and discharging of all battery packs on the basis of the first stage scheduling results. The proposed loss-calculation method is used to calculate the battery pack loss and then to calculate the SOHs of battery packs. It can be seen from the simulation results that the scheduling results considering the SOH are more realistic. Furthermore, the proposed charging and discharging allocation strategy can effectively coordinate the SOH change of all battery packs without causing a significant increase in the battery pack loss of the battery packs.

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Earth Observation Satellite Scheduling Problem for Multitemporal Revisit Tasks: A Variable Neighborhood Search Algorithm

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SSRN 2024年

作者： Xing, Ligang Hu, Xiaoxuan Zhu, Waiming Zhu, Xutong Xia, Wei School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Engineering Research Center for Intelligent Management of Aerospace System Hefei230009 China

After natural disasters such as floods and earthquakes, Earth observation satellites (EOSs) often need to revisit affected areas multiple times to acquire multitemporal images. Such observation tasks are referred to as multitemporal revisit tasks. In this paper, we study the Earth observation satellite scheduling problem for multitemporal revisit tasks, which is a combinatorial optimization problem in continuous space. This problem exhibits more complex constraints and is more challenging to solve than the traditional Earth observation satellite scheduling problem. Firstly, we formally define the problem and formulate it with a mixed integer nonlinear programming model. Secondly, we develop a variable neighborhood search algorithm to search for the optimal solution. This algorithm embeds a dynamic greedy heuristic, which can efficiently generate a schedule for satellites. Thirdly, computational experiments demonstrate the efficiency and stability of the algorithm. Moreover, the CPU time of the algorithm increases linearly with both task count and revisit number, indicating good scalability. © 2024, The Authors. All rights reserved.

关键词： Integer programming

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Probabilistic Electricity Price Forecasting and Uncertainty Estimation Using Deep Bayesian Model with Multi-Distribution Fusion

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SSRN 2024年

作者： Shao, Zhen Han, Yating Fu, Chao Zha, Jianrui Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Hefei University of Technology Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Probabilistic electricity price forecasting (EPF) is paramount for stakeholder scheduling and trading in deregulated energy markets. However, during the process of establishing a probability prediction model, the consequential aleatoric and epistemic uncertainties are indistinguishable. Moreover, identifying epistemic uncertainty rather than aleatoric uncertainty is significant for conducting credible probabilistic EPF in cases with expected and unexpected fluctuations. This article proposes a hybrid approach under a Bayesian probability framework for performing reliable and accurate probabilistic forecasting. Specifically, it applies Bayesian deep learning model to convey the latent probability distribution of electricity prices, which represents the desirable epistemic uncertainty and inevitable aleatoric uncertainty. The embedded architecture employs a multi-head attention mechanism (MHAM) to allocate previous hidden states of long short-term memory (LSTM) cells, detecting both the local and global price patterns simultaneously. Considering the forecasting bias arising from aleatoric uncertainty in the process of establishing Bayesian networks, a new Wasserstein distance-based multi-distribution fusion mechanism driven by evidence theory is proposed and employed to identify and mitigate the aleatoric uncertainty of credible probabilistic EPF problems. The effectiveness of the proposed approach is evaluated on two real-world electricity price datasets and compared with twelve state-of-the-art models. © 2024, The Authors. All rights reserved.

关键词： Bayesian networks

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An Integrated Position-velocity-force Method for Safety-enhanced Shared Control in Robot-assisted Surgical Cutting

An Integrated Position-velocity-force Method for Safety-enha...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xilin Xiao Xiaojian Li Yudong Shi Jin Fang Lin Li Pengfei He Hangjie Mo School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Hefei China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Numerous studies have emphasized the application of autonomous intelligence in human-robot shared control to enhance surgical convenience and efficiency. However, the neglect of human dominance may reduce surgical safety. This paper developed a safety-enhanced human-robot shared control method by intelligently allocating control authority, with the surgeon remaining the leader during the surgical procedure. Three controllers are designed initially, including a master hand position (MP) controller and a master hand velocity (MV) controller related to the surgeon's manipulation, and a planned trajectory tracking (PT) controller related to the robot. In precision surgical manipulation scenarios, precise tracking of the human's operation is achieved by combining MP and MV controllers, while a combination of MV and PT controllers is developed in high-efficiency surgical scenarios, which relaxes the requirement for precise tracking of hand position and enables precise robot assistance guided by the velocity of human hand. The autonomous scenarios and controllers switching are accomplished through a motion fusion mechanism, which is achieved via optimizing evaluation functions that are reliant on future states. Furthermore, a force feedback mechanism is proposed to help human understand the intent of autonomous control to improve safety. The feasibility and effectiveness of this method have been validated through simulations and experiments.

关键词： Medical robotics Trajectory tracking Force feedback Dynamics Surgery Switches Controllability

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Multi-keyframe Guided Points Tracking Method for Enhanced Robotic Surgery

Multi-Keyframe Guided Points Tracking Method for Enhanced Ro...

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IEEE International Conference on Robotics and Biomimetics

作者： Xiaojian Li Tongtong Li Xiang Ma Ling Li Hangjie Mo Jieyu Zheng School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Hefei China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Points tracking provides target guidance for the basic tasks of robotic surgery, such as autonomous cutting, autonomous suturing, and surgical navigation. Existing invivo tracking algorithms mainly consider region-level tracking, making it difficult to achieve pixel-level accuracy. This paper proposes a multi-keyframe guided tracking method (MKTracking) for robotic surgery, which achieves long-term dynamic tracking of any point on endoscopic images. An incremental keyframe pool building block based on time interval and image appearance is proposed, which contains keyframe basic information, tracking patch and tracking points. Besides, an online multi-keyframe query mechanism is designed to efficiently retrieves the three most similar templates from the keyframe pool based on the number of feature matches within the target patch. This mechanism can realize the identification of target moving out and the re-tracking of target reappearance. Points tracking result is obtained by fusing the optical flow results within the target patch across multiple frames. We evaluated MKTracking using ground-truth data, and the results demonstrated that our approach achieved superior tracking performance.

关键词： Target tracking Accuracy Minimally invasive surgery Navigation Heuristic algorithms Buildings Real-time systems Robots Optical flow Videos

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Pain Intensity Recognition from Masked Facial Expressions using Swin-Transformer

Pain Intensity Recognition from Masked Facial Expressions us...

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IEEE International Conference on Robotics and Biomimetics

作者： Xin Yuan Shuai Zhang Cancan Zhao Xian He Bo Ouyang Shanlin Yang Key Laboratory of Process Optimization and Intelligent Decision-Making(Ministry of Education) School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665481106

Previous studies recognize pain expressions based on the entire face, for example, Prkachin and Solomon Pain intensity (PSPI). However, the patients face is often masked by instruments in an intensive care unit (ICU), such as respirator, and gauzes, just name a few, which causes the agent cannot measure the pain intensity using PSPI directly. To tackle this problem, we explore the pain intensity recognition from masked face. First, we conducted four levels of pain measurement experiments with four types of masked face using Swin-Transformer. Experiment results show that the accuracy of pain measurement is more than 90%, even masking a large part of facial Action Units (AU) related to the pain on the UNBC-McMaster dataset. Furthermore, we pre-train the pain recognition model with masked face dataset, such that the model can capture facial features contributed to the pain. Results on binary and four-level pain intensity measurement tasks show our model outperforms recent state-of-the-art performance, achieving 97.38% and 95.25% accuracy, respectively.

关键词： Gold Pain Face recognition Instruments Ventilation Robustness Physiology

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Multi-Objective optimization of Cloud Manufacturing Service Combination Based on Manufacturing and Logistics Coordinate

Multi-Objective Optimization of Cloud Manufacturing Service ...

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Chinese Control and decision Conference, CCDC

作者： Sining Han Bingfeng Ge Wanying Wei Xiaonong Lu Zeqiang Hou Yuming Huang College of Systems Engineering National University of Defense Technology National University of Defense Technology Changsha China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education University of Science and Technology of China Hefei China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

With the continuous emergence of new technologies, the manufacturing industry is facing unprecedented challenges. Manufacturing services typically involve various types of production equipment, diverse suppliers, and varied customer demands. optimization in this context often encompasses multi pie objectives, necessitating timely adjustments in service comb ¡nations to meet customer needs. Traditional manufacturing models evidently fall short in resource allocation within manufacturing supply chains. Cloud manufacturing, proposed as a novel manufacturing model, aims to optimize resource allocation through the combination and configuration of resources, there by enhancing the efficiency of manufacturing supply chains. Logistics, as a crucial component within manufacturing supply chains, underscores the significance of coordinating the optimizad on of manufacturing and logistics services. This research focus es on the multi-objective optimization problem of cloud manufacturing service combinations. Building upon the execution process of manufacturing and logistics services for complex product manufacturing tasks, a novel mathematical model for the optimization of manufacturing resources and logistics resources i s constructed. The model utilizes the NSGA-H algorithm to coordinate the optimization processes of manufacturing and legistics resources. Finally, a case study demonstrates the effectiveness of the proposed model.

关键词： Costs Supply chains Production equipment Pareto optimization Mathematical models Manufacturing Resource management

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The impacts of content and source factors on consumers’ liking toward advertisements: an hsm-based framework

The impacts of content and source factors on consumers’ lik...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Chu, Xiumin Liu, Yezheng Chen, Xiayu School of Management Hefei University of Technology HefeiAnhui230009 China Key Laboratory of Process Optimization and Intelligent Decision Making Ministry of Education HefeiAnhui230009 China

ISBN: (纸本)9783030539795

Drawing on heuristic-systematic model (HSM), this study develops a research model to examine how content factors (i.e., persuasive and informative cues) and source factors (i.e., brand popularity and brand reputation) affect consumers’ liking behavior toward advertisements in microblogs, specifically on Weibo. Brand type is regarded as a moderator in the research model. Manual coding and machine learning algorithms are integrated to develop a classification model that tags tweets. Our study enhances the current understanding of consumers’ liking behavior on social media and provides managerial insights for brands seeking to facilitate consumer engagement. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Learning algorithms

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Robust PD-type iterative learning control design for uncertain batch processes subject to nonrepetitive disturbances 41

Robust PD-type iterative learning control design for uncerta...

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第41届中国控制会议

作者： Robert Maniarski Wojciech Paszke Shoulin Hao Hongfeng Tao Institute of Automation Electronic and Electrical EngineeringUniversity of Zielona Góra Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper develops PD-type iterative learning control schemes for a class of uncertain batch processes subject to nonrepetitive *** means of two-dimensional/repetitive setting,the sufficient conditions for batch-to-batch error convergence and H disturbance attenuation are formulated and ***,the procedure for computing the desired control law matrices is formulated in terms of solvability of linear matrix *** proposed control law is able to fulfil the imposed design specifications,i.e.,they are suitable for the batch processes with time-varying uncertainties as well as non-repetitive *** illustrative example is used to validate the proposed control scheme and demonstrates a possible applicability of the developed results.

关键词： iterative learning control repetitive processes batch processes robust control nonrepetitive disturbances

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A Transfer-Based decision-making Method with Criterion Weight and Reliability Transfer

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SSRN 2023年

作者： Jia, Xuefei Chang, Wenjun Liao, Baoyu School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

The accumulated historical data not only brings benefits to social life, but also increases the complexity of decision-making. Data-driven multi-criteria decision-making (D2MCDM) can extract parameter information from historical data to help decision-makers generate acceptable and satisfactory decision solutions. When little historical data in one domain is available, the extracted parameter information may be inaccurate or inconsistency. To deal with this issue, the parameter-based transfer learning is considered to integrate into D2MCDM. The domain with little historical data is called the target domain (TD). One related domain with sufficient data is found, which is called the SD (SD). The sufficient data in the SD can help extract accurate parameter information of the TD. This paper proposes a transfer-based decision-making (TBDM) method with criterion weight and reliability transfer based on the Tri-training algorithm. The Tri-training algorithm is used to construct a data selection stage for the SD, which improves the data similarity between the SD and TD. The data-driven decision-making models are then constructed to learn criterion weights and reliabilities. The proposed TBDM method not only deals with the problem of insufficient data for decision model training, but also improves the acceptability of decision solutions to decision-makers. A case study of the diagnosis of breast lesions is analyzed to demonstrate the advantages of the TBDM method. The UCI datasets are used to conduct the comparative experiments to further validate the advantages of the TBDM method. © 2023, The Authors. All rights reserved.

关键词： Reliability

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